XlogP

XlogP is a family of computational models used to estimate the lipophilicity of organic molecules, expressed as the logarithm of the octanol–water partition coefficient (logP). Lipophilicity is a key property in pharmacokinetics and environmental fate, and XlogP enables rapid prediction from a molecule’s 2D structure, supporting screening and modeling tasks in medicinal chemistry and toxicology.

Most XlogP models are fragment- or atom-based; the program assigns contribution values to chemical fragments or atoms and sums them to produce a logP estimate. Additional empirical correction terms account for interactions among fragments, ring systems, heteroatoms, and specific functional groups that influence polarity and solvation. Over successive versions, the method expanded coverage of chemical space and improved accuracy. Common variants include XlogP2 and XlogP3, with XlogP3 often available in an all-atom flavor, designed to better handle diverse chemistries.

XlogP is widely used in structure-activity relationship studies, lead optimization, and property prediction workflows. It is one among several logP prediction approaches, alongside other fragment-based constants and machine-learned models. Limitations include dependence on training data, reduced accuracy for salts or highly unusual structures, and potential biases for certain chemical classes. For critical decisions, users should corroborate lipophilicity estimates with experimental measurements or alternative computational methods.